期刊
KNOWLEDGE AND INFORMATION SYSTEMS
卷 6, 期 2, 页码 164-187出版社
SPRINGER LONDON LTD
DOI: 10.1007/s10115-003-0107-8
关键词
Bayesian network; collective data mining; distributed data mining; heterogeneous data; web log mining
We present a collective approach to learning a Bayesian network from distributed heterogeneous data. In this approach, we first learn a local Bayesian network at each site using the local data. Then each site identifies the observations that are most likely to be evidence of coupling between local and non-local variables and transmits a subset of these observations to a central site. Another Bayesian network is learnt at the central site using the data transmitted from the local site. The local and central Bayesian networks are combined to obtain a collective Bayesian network, which models the entire data. Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.
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